Overview

Dataset statistics

Number of variables14
Number of observations16743
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory112.0 B

Variable types

Numeric8
DateTime1
Categorical1
Text4

Alerts

Data.Temperature.Avg Temp is highly overall correlated with Data.Temperature.Max Temp and 1 other fieldsHigh correlation
Data.Temperature.Max Temp is highly overall correlated with Data.Temperature.Avg Temp and 1 other fieldsHigh correlation
Data.Temperature.Min Temp is highly overall correlated with Data.Temperature.Avg Temp and 1 other fieldsHigh correlation
Date.Year is highly imbalanced (86.5%)Imbalance
Data.Precipitation has 4324 (25.8%) zerosZeros
Data.Wind.Direction has 223 (1.3%) zerosZeros
Data.Wind.Speed has 223 (1.3%) zerosZeros

Reproduction

Analysis started2024-01-13 09:43:13.148313
Analysis finished2024-01-13 09:43:21.223820
Duration8.08 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Data.Precipitation
Real number (ℝ)

ZEROS 

Distinct565
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57909037
Minimum0
Maximum20.89
Zeros4324
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:21.312639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.19
Q30.75
95-th percentile2.38
Maximum20.89
Range20.89
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.98805716
Coefficient of variation (CV)1.7062228
Kurtosis34.213837
Mean0.57909037
Median Absolute Deviation (MAD)0.19
Skewness4.2079949
Sum9695.71
Variance0.97625695
MonotonicityNot monotonic
2024-01-13T15:13:21.455169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4324
25.8%
0.01 549
 
3.3%
0.02 366
 
2.2%
0.03 333
 
2.0%
0.04 279
 
1.7%
0.05 249
 
1.5%
0.06 205
 
1.2%
0.11 199
 
1.2%
0.07 195
 
1.2%
0.08 194
 
1.2%
Other values (555) 9850
58.8%
ValueCountFrequency (%)
0 4324
25.8%
0.01 549
 
3.3%
0.02 366
 
2.2%
0.03 333
 
2.0%
0.04 279
 
1.7%
0.05 249
 
1.5%
0.06 205
 
1.2%
0.07 195
 
1.2%
0.08 194
 
1.2%
0.09 193
 
1.2%
ValueCountFrequency (%)
20.89 1
< 0.1%
15.19 1
< 0.1%
14.03 1
< 0.1%
13.36 1
< 0.1%
12.65 1
< 0.1%
12.02 1
< 0.1%
11.52 1
< 0.1%
10.58 1
< 0.1%
10.49 1
< 0.1%
10.34 1
< 0.1%
Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
Minimum2016-01-03 00:00:00
Maximum2017-01-01 00:00:00
2024-01-13T15:13:21.589647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:21.729891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Date.Month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3431285
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:21.933403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.490723
Coefficient of variation (CV)0.55031566
Kurtosis-1.2144816
Mean6.3431285
Median Absolute Deviation (MAD)3
Skewness0.0069942293
Sum106203
Variance12.185147
MonotonicityNot monotonic
2024-01-13T15:13:22.044900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1892
11.3%
5 1585
9.5%
10 1585
9.5%
7 1572
9.4%
8 1268
7.6%
12 1268
7.6%
4 1265
7.6%
9 1265
7.6%
3 1262
7.5%
6 1262
7.5%
Other values (2) 2519
15.0%
ValueCountFrequency (%)
1 1892
11.3%
2 1260
7.5%
3 1262
7.5%
4 1265
7.6%
5 1585
9.5%
6 1262
7.5%
7 1572
9.4%
8 1268
7.6%
9 1265
7.6%
10 1585
9.5%
ValueCountFrequency (%)
12 1268
7.6%
11 1259
7.5%
10 1585
9.5%
9 1265
7.6%
8 1268
7.6%
7 1572
9.4%
6 1262
7.5%
5 1585
9.5%
4 1265
7.6%
3 1262
7.5%

Date.Week of
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.650242
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:22.161991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9234249
Coefficient of variation (CV)0.57017808
Kurtosis-1.1987441
Mean15.650242
Median Absolute Deviation (MAD)8
Skewness0.024204995
Sum262032
Variance79.627513
MonotonicityNot monotonic
2024-01-13T15:13:22.288862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 948
 
5.7%
17 945
 
5.6%
24 945
 
5.6%
10 945
 
5.6%
6 634
 
3.8%
1 634
 
3.8%
4 634
 
3.8%
18 633
 
3.8%
25 633
 
3.8%
11 633
 
3.8%
Other values (21) 9159
54.7%
ValueCountFrequency (%)
1 634
3.8%
2 317
 
1.9%
3 948
5.7%
4 634
3.8%
5 317
 
1.9%
6 634
3.8%
7 632
3.8%
8 317
 
1.9%
9 317
 
1.9%
10 945
5.6%
ValueCountFrequency (%)
31 629
3.8%
30 317
 
1.9%
29 317
 
1.9%
28 632
3.8%
27 629
3.8%
26 315
 
1.9%
25 633
3.8%
24 945
5.6%
23 317
 
1.9%
22 317
 
1.9%

Date.Year
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2016
16426 
2017
 
317

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters66972
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 16426
98.1%
2017 317
 
1.9%

Length

2024-01-13T15:13:22.414259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-13T15:13:22.521255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 16426
98.1%
2017 317
 
1.9%

Most occurring characters

ValueCountFrequency (%)
2 16743
25.0%
0 16743
25.0%
1 16743
25.0%
6 16426
24.5%
7 317
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16743
25.0%
0 16743
25.0%
1 16743
25.0%
6 16426
24.5%
7 317
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 66972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 16743
25.0%
0 16743
25.0%
1 16743
25.0%
6 16426
24.5%
7 317
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 16743
25.0%
0 16743
25.0%
1 16743
25.0%
6 16426
24.5%
7 317
 
0.5%
Distinct307
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:22.729280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length8.8165203
Min length3

Characters and Unicode

Total characters147615
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBirmingham
2nd rowHuntsville
3rd rowMobile
4th rowMontgomery
5th rowAnchorage
ValueCountFrequency (%)
city 477
 
2.2%
san 265
 
1.2%
falls 265
 
1.2%
lake 253
 
1.2%
beach 212
 
1.0%
fort 212
 
1.0%
st 212
 
1.0%
grand 212
 
1.0%
island 159
 
0.7%
santa 106
 
0.5%
Other values (337) 18853
88.8%
2024-01-13T15:13:23.099348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13728
 
9.3%
e 11833
 
8.0%
o 10641
 
7.2%
n 10605
 
7.2%
l 9902
 
6.7%
i 9296
 
6.3%
r 8292
 
5.6%
t 8190
 
5.5%
s 6818
 
4.6%
u 4753
 
3.2%
Other values (45) 53557
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 120481
81.6%
Uppercase Letter 21862
 
14.8%
Space Separator 4483
 
3.0%
Other Punctuation 577
 
0.4%
Dash Punctuation 106
 
0.1%
Open Punctuation 53
 
< 0.1%
Close Punctuation 53
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13728
11.4%
e 11833
9.8%
o 10641
8.8%
n 10605
8.8%
l 9902
 
8.2%
i 9296
 
7.7%
r 8292
 
6.9%
t 8190
 
6.8%
s 6818
 
5.7%
u 4753
 
3.9%
Other values (15) 26423
21.9%
Uppercase Letter
ValueCountFrequency (%)
C 2430
 
11.1%
S 2222
 
10.2%
B 1961
 
9.0%
A 1325
 
6.1%
M 1313
 
6.0%
P 1212
 
5.5%
L 1207
 
5.5%
W 1190
 
5.4%
G 1161
 
5.3%
R 1056
 
4.8%
Other values (14) 6785
31.0%
Other Punctuation
ValueCountFrequency (%)
/ 530
91.9%
. 47
 
8.1%
Space Separator
ValueCountFrequency (%)
4483
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%
Open Punctuation
ValueCountFrequency (%)
( 53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 142343
96.4%
Common 5272
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13728
 
9.6%
e 11833
 
8.3%
o 10641
 
7.5%
n 10605
 
7.5%
l 9902
 
7.0%
i 9296
 
6.5%
r 8292
 
5.8%
t 8190
 
5.8%
s 6818
 
4.8%
u 4753
 
3.3%
Other values (39) 48285
33.9%
Common
ValueCountFrequency (%)
4483
85.0%
/ 530
 
10.1%
- 106
 
2.0%
( 53
 
1.0%
) 53
 
1.0%
. 47
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13728
 
9.3%
e 11833
 
8.0%
o 10641
 
7.2%
n 10605
 
7.2%
l 9902
 
6.7%
i 9296
 
6.3%
r 8292
 
5.6%
t 8190
 
5.5%
s 6818
 
4.6%
u 4753
 
3.2%
Other values (45) 53557
36.3%
Distinct318
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:23.445881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters50229
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBHM
2nd rowHSV
3rd rowMOB
4th rowMGM
5th rowANC
ValueCountFrequency (%)
bhm 53
 
0.3%
gkn 53
 
0.3%
ann 53
 
0.3%
bet 53
 
0.3%
btt 53
 
0.3%
cdb 53
 
0.3%
inw 53
 
0.3%
cdv 53
 
0.3%
fai 53
 
0.3%
hom 53
 
0.3%
Other values (308) 16213
96.8%
2024-01-13T15:13:23.908585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3865
 
7.7%
S 3804
 
7.6%
L 3286
 
6.5%
T 3061
 
6.1%
B 2756
 
5.5%
C 2756
 
5.5%
G 2566
 
5.1%
N 2535
 
5.0%
D 2479
 
4.9%
I 2426
 
4.8%
Other values (19) 20695
41.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 50017
99.6%
Decimal Number 212
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3865
 
7.7%
S 3804
 
7.6%
L 3286
 
6.6%
T 3061
 
6.1%
B 2756
 
5.5%
C 2756
 
5.5%
G 2566
 
5.1%
N 2535
 
5.1%
D 2479
 
5.0%
I 2426
 
4.9%
Other values (16) 20483
41.0%
Decimal Number
ValueCountFrequency (%)
8 106
50.0%
6 53
25.0%
2 53
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50017
99.6%
Common 212
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3865
 
7.7%
S 3804
 
7.6%
L 3286
 
6.6%
T 3061
 
6.1%
B 2756
 
5.5%
C 2756
 
5.5%
G 2566
 
5.1%
N 2535
 
5.1%
D 2479
 
5.0%
I 2426
 
4.9%
Other values (16) 20483
41.0%
Common
ValueCountFrequency (%)
8 106
50.0%
6 53
25.0%
2 53
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3865
 
7.7%
S 3804
 
7.6%
L 3286
 
6.5%
T 3061
 
6.1%
B 2756
 
5.5%
C 2756
 
5.5%
G 2566
 
5.1%
N 2535
 
5.0%
D 2479
 
4.9%
I 2426
 
4.8%
Other values (19) 20695
41.2%
Distinct318
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:24.166453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length12.81652
Min length7

Characters and Unicode

Total characters214587
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBirmingham, AL
2nd rowHuntsville, AL
3rd rowMobile, AL
4th rowMontgomery, AL
5th rowAnchorage, AK
ValueCountFrequency (%)
ak 1719
 
4.5%
tx 1272
 
3.4%
ca 999
 
2.6%
fl 636
 
1.7%
mt 630
 
1.7%
mi 477
 
1.3%
city 477
 
1.3%
or 424
 
1.1%
ne 424
 
1.1%
ny 424
 
1.1%
Other values (386) 30487
80.3%
2024-01-13T15:13:24.552202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21226
 
9.9%
, 16743
 
7.8%
a 13728
 
6.4%
e 11833
 
5.5%
o 10641
 
5.0%
n 10605
 
4.9%
l 9902
 
4.6%
i 9296
 
4.3%
r 8292
 
3.9%
t 8190
 
3.8%
Other values (48) 94131
43.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 120481
56.1%
Uppercase Letter 55348
25.8%
Space Separator 21226
 
9.9%
Other Punctuation 17320
 
8.1%
Dash Punctuation 106
 
< 0.1%
Open Punctuation 53
 
< 0.1%
Close Punctuation 53
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6789
 
12.3%
C 4436
 
8.0%
M 3957
 
7.1%
N 3647
 
6.6%
S 3388
 
6.1%
K 3203
 
5.8%
T 2964
 
5.4%
L 2634
 
4.8%
I 2332
 
4.2%
W 2250
 
4.1%
Other values (16) 19748
35.7%
Lowercase Letter
ValueCountFrequency (%)
a 13728
11.4%
e 11833
9.8%
o 10641
8.8%
n 10605
8.8%
l 9902
 
8.2%
i 9296
 
7.7%
r 8292
 
6.9%
t 8190
 
6.8%
s 6818
 
5.7%
u 4753
 
3.9%
Other values (15) 26423
21.9%
Other Punctuation
ValueCountFrequency (%)
, 16743
96.7%
/ 530
 
3.1%
. 47
 
0.3%
Space Separator
ValueCountFrequency (%)
21226
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%
Open Punctuation
ValueCountFrequency (%)
( 53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 175829
81.9%
Common 38758
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13728
 
7.8%
e 11833
 
6.7%
o 10641
 
6.1%
n 10605
 
6.0%
l 9902
 
5.6%
i 9296
 
5.3%
r 8292
 
4.7%
t 8190
 
4.7%
s 6818
 
3.9%
A 6789
 
3.9%
Other values (41) 79735
45.3%
Common
ValueCountFrequency (%)
21226
54.8%
, 16743
43.2%
/ 530
 
1.4%
- 106
 
0.3%
( 53
 
0.1%
) 53
 
0.1%
. 47
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 214587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21226
 
9.9%
, 16743
 
7.8%
a 13728
 
6.4%
e 11833
 
5.5%
o 10641
 
5.0%
n 10605
 
4.9%
l 9902
 
4.6%
i 9296
 
4.3%
r 8292
 
3.9%
t 8190
 
3.8%
Other values (48) 94131
43.9%
Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:24.742991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.932091
Min length2

Characters and Unicode

Total characters132807
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlabama
3rd rowAlabama
4th rowAlabama
5th rowAlaska
ValueCountFrequency (%)
alaska 1719
 
9.1%
texas 1272
 
6.7%
california 999
 
5.3%
new 789
 
4.2%
florida 636
 
3.4%
north 636
 
3.4%
carolina 583
 
3.1%
montana 583
 
3.1%
dakota 530
 
2.8%
virginia 530
 
2.8%
Other values (46) 10686
56.4%
2024-01-13T15:13:25.067264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20409
15.4%
i 13114
 
9.9%
n 10686
 
8.0%
s 10528
 
7.9%
o 10376
 
7.8%
e 7492
 
5.6%
r 6558
 
4.9%
l 5898
 
4.4%
t 4016
 
3.0%
k 3733
 
2.8%
Other values (37) 39997
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111518
84.0%
Uppercase Letter 19069
 
14.4%
Space Separator 2220
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20409
18.3%
i 13114
11.8%
n 10686
9.6%
s 10528
9.4%
o 10376
9.3%
e 7492
 
6.7%
r 6558
 
5.9%
l 5898
 
5.3%
t 4016
 
3.6%
k 3733
 
3.3%
Other values (14) 18708
16.8%
Uppercase Letter
ValueCountFrequency (%)
M 2644
13.9%
A 2408
12.6%
N 2167
11.4%
C 2006
10.5%
T 1692
8.9%
W 1060
 
5.6%
O 1007
 
5.3%
I 1007
 
5.3%
F 636
 
3.3%
D 636
 
3.3%
Other values (12) 3806
20.0%
Space Separator
ValueCountFrequency (%)
2220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130587
98.3%
Common 2220
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20409
15.6%
i 13114
 
10.0%
n 10686
 
8.2%
s 10528
 
8.1%
o 10376
 
7.9%
e 7492
 
5.7%
r 6558
 
5.0%
l 5898
 
4.5%
t 4016
 
3.1%
k 3733
 
2.9%
Other values (36) 37777
28.9%
Common
ValueCountFrequency (%)
2220
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20409
15.4%
i 13114
 
9.9%
n 10686
 
8.0%
s 10528
 
7.9%
o 10376
 
7.8%
e 7492
 
5.6%
r 6558
 
4.9%
l 5898
 
4.4%
t 4016
 
3.0%
k 3733
 
2.8%
Other values (37) 39997
30.1%

Data.Temperature.Avg Temp
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.089112
Minimum-27
Maximum100
Zeros15
Zeros (%)0.1%
Negative79
Negative (%)0.5%
Memory size130.9 KiB
2024-01-13T15:13:25.219925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-27
5-th percentile23
Q144
median58
Q371
95-th percentile83
Maximum100
Range127
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.798295
Coefficient of variation (CV)0.33515051
Kurtosis-0.013455145
Mean56.089112
Median Absolute Deviation (MAD)14
Skewness-0.55250384
Sum939100
Variance353.37588
MonotonicityNot monotonic
2024-01-13T15:13:25.370240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 643
 
3.8%
58 338
 
2.0%
74 329
 
2.0%
57 328
 
2.0%
56 325
 
1.9%
72 322
 
1.9%
75 320
 
1.9%
55 319
 
1.9%
59 315
 
1.9%
61 314
 
1.9%
Other values (109) 13190
78.8%
ValueCountFrequency (%)
-27 1
 
< 0.1%
-21 2
< 0.1%
-20 1
 
< 0.1%
-19 2
< 0.1%
-18 3
< 0.1%
-15 1
 
< 0.1%
-14 1
 
< 0.1%
-13 3
< 0.1%
-11 2
< 0.1%
-10 1
 
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 1
 
< 0.1%
98 4
 
< 0.1%
97 2
 
< 0.1%
95 1
 
< 0.1%
94 3
 
< 0.1%
93 8
< 0.1%
92 7
< 0.1%
91 10
0.1%
90 13
0.1%

Data.Temperature.Max Temp
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.042406
Minimum-19
Maximum111
Zeros6
Zeros (%)< 0.1%
Negative31
Negative (%)0.2%
Memory size130.9 KiB
2024-01-13T15:13:25.517088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile30
Q153
median68
Q382
95-th percentile93
Maximum111
Range130
Interquartile range (IQR)29

Descriptive statistics

Standard deviation19.787954
Coefficient of variation (CV)0.29962497
Kurtosis-0.056357295
Mean66.042406
Median Absolute Deviation (MAD)14
Skewness-0.5928578
Sum1105748
Variance391.56313
MonotonicityNot monotonic
2024-01-13T15:13:25.667384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 592
 
3.5%
82 390
 
2.3%
84 366
 
2.2%
83 363
 
2.2%
81 360
 
2.2%
86 359
 
2.1%
80 349
 
2.1%
85 335
 
2.0%
70 325
 
1.9%
66 311
 
1.9%
Other values (115) 12993
77.6%
ValueCountFrequency (%)
-19 1
 
< 0.1%
-16 1
 
< 0.1%
-15 1
 
< 0.1%
-11 4
< 0.1%
-10 2
< 0.1%
-9 1
 
< 0.1%
-8 1
 
< 0.1%
-7 2
< 0.1%
-6 1
 
< 0.1%
-5 3
< 0.1%
ValueCountFrequency (%)
111 2
 
< 0.1%
110 4
 
< 0.1%
109 4
 
< 0.1%
107 6
 
< 0.1%
106 4
 
< 0.1%
105 9
 
0.1%
104 13
0.1%
103 22
0.1%
102 13
0.1%
101 29
0.2%

Data.Temperature.Min Temp
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.642716
Minimum-35
Maximum88
Zeros40
Zeros (%)0.2%
Negative214
Negative (%)1.3%
Memory size130.9 KiB
2024-01-13T15:13:25.808589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35
5-th percentile14
Q133
median47
Q360
95-th percentile73
Maximum88
Range123
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.559263
Coefficient of variation (CV)0.40662047
Kurtosis-0.11371736
Mean45.642716
Median Absolute Deviation (MAD)13
Skewness-0.43089864
Sum764196
Variance344.44623
MonotonicityNot monotonic
2024-01-13T15:13:25.956228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 550
 
3.3%
52 343
 
2.0%
49 331
 
2.0%
47 328
 
2.0%
46 325
 
1.9%
51 324
 
1.9%
54 320
 
1.9%
53 318
 
1.9%
58 316
 
1.9%
45 314
 
1.9%
Other values (104) 13274
79.3%
ValueCountFrequency (%)
-35 1
 
< 0.1%
-28 2
< 0.1%
-27 2
< 0.1%
-26 3
< 0.1%
-24 1
 
< 0.1%
-23 1
 
< 0.1%
-21 3
< 0.1%
-20 3
< 0.1%
-19 3
< 0.1%
-18 2
< 0.1%
ValueCountFrequency (%)
88 2
 
< 0.1%
87 2
 
< 0.1%
85 4
 
< 0.1%
83 2
 
< 0.1%
82 10
 
0.1%
81 13
 
0.1%
80 32
 
0.2%
79 44
0.3%
78 74
0.4%
77 92
0.5%

Data.Wind.Direction
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.791316
Minimum0
Maximum36
Zeros223
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:26.090521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q115
median19
Q323
95-th percentile28
Maximum36
Range36
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.4615273
Coefficient of variation (CV)0.3438571
Kurtosis0.21101361
Mean18.791316
Median Absolute Deviation (MAD)4
Skewness-0.48168313
Sum314623
Variance41.751335
MonotonicityNot monotonic
2024-01-13T15:13:26.229684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
21 1134
 
6.8%
20 1087
 
6.5%
22 1065
 
6.4%
19 1046
 
6.2%
18 991
 
5.9%
23 990
 
5.9%
17 916
 
5.5%
16 896
 
5.4%
24 862
 
5.1%
25 777
 
4.6%
Other values (27) 6979
41.7%
ValueCountFrequency (%)
0 223
1.3%
1 21
 
0.1%
2 48
 
0.3%
3 63
 
0.4%
4 126
0.8%
5 146
0.9%
6 135
0.8%
7 168
1.0%
8 228
1.4%
9 310
1.9%
ValueCountFrequency (%)
36 16
 
0.1%
35 20
 
0.1%
34 32
 
0.2%
33 42
 
0.3%
32 76
 
0.5%
31 126
 
0.8%
30 183
 
1.1%
29 263
1.6%
28 397
2.4%
27 519
3.1%

Data.Wind.Speed
Real number (ℝ)

ZEROS 

Distinct1461
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3298202
Minimum0
Maximum61.1
Zeros223
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size130.9 KiB
2024-01-13T15:13:26.372709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.81
Q14.04
median5.94
Q38.08
95-th percentile11.95
Maximum61.1
Range61.1
Interquartile range (IQR)4.04

Descriptive statistics

Standard deviation3.4947853
Coefficient of variation (CV)0.55211446
Kurtosis20.740846
Mean6.3298202
Median Absolute Deviation (MAD)2
Skewness2.4603892
Sum105980.18
Variance12.213524
MonotonicityNot monotonic
2024-01-13T15:13:26.605342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 223
 
1.3%
5.7 48
 
0.3%
5.6 48
 
0.3%
7.2 46
 
0.3%
6.3 46
 
0.3%
5.9 46
 
0.3%
5.1 46
 
0.3%
5.4 45
 
0.3%
4.7 45
 
0.3%
4.6 44
 
0.3%
Other values (1451) 16106
96.2%
ValueCountFrequency (%)
0 223
1.3%
0.15 1
 
< 0.1%
0.18 2
 
< 0.1%
0.2 2
 
< 0.1%
0.25 2
 
< 0.1%
0.26 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3 6
 
< 0.1%
0.32 1
 
< 0.1%
0.34 1
 
< 0.1%
ValueCountFrequency (%)
61.1 1
< 0.1%
52.6 1
< 0.1%
50.06 1
< 0.1%
49.6 1
< 0.1%
47.56 1
< 0.1%
47.2 1
< 0.1%
45.16 1
< 0.1%
43.85 1
< 0.1%
43.34 1
< 0.1%
43.25 1
< 0.1%

Interactions

2024-01-13T15:13:20.039137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:14.328413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.114207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.907934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.716018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.529750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.422876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.223630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:20.132322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:14.421160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.207161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.002192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.811741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.624003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.516144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.318202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:20.230610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:14.516243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.301522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.099977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.911009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.723421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.613362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.416380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:20.330356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:14.614861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.401351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.202046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.013222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.825370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.715108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.519074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:20.432132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:14.714370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.502265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.304915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.115807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.927969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.815634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.622087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:20.532449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:14.814434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.603407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.408611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.218583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.028173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.917946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.727942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:20.633645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:14.914140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.703954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.510225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.321671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.129771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.017735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.830408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:20.738373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.016256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:15.808298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:16.614299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:17.427603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:18.235842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.122745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-13T15:13:19.936319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-01-13T15:13:26.704646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Data.PrecipitationData.Temperature.Avg TempData.Temperature.Max TempData.Temperature.Min TempData.Wind.DirectionData.Wind.SpeedDate.MonthDate.Week ofDate.Year
Data.Precipitation1.0000.032-0.0270.094-0.1150.037-0.0180.1230.000
Data.Temperature.Avg Temp0.0321.0000.9770.975-0.195-0.1810.2380.0500.131
Data.Temperature.Max Temp-0.0270.9771.0000.909-0.151-0.2040.2310.0620.131
Data.Temperature.Min Temp0.0940.9750.9091.000-0.233-0.1500.2360.0370.121
Data.Wind.Direction-0.115-0.195-0.151-0.2331.0000.060-0.086-0.0310.070
Data.Wind.Speed0.037-0.181-0.204-0.1500.0601.000-0.133-0.0080.056
Date.Month-0.0180.2380.2310.236-0.086-0.1331.0000.0220.288
Date.Week of0.1230.0500.0620.037-0.031-0.0080.0221.0000.328
Date.Year0.0000.1310.1310.1210.0700.0560.2880.3281.000

Missing values

2024-01-13T15:13:20.887332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-13T15:13:21.113839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Data.PrecipitationDate.FullDate.MonthDate.Week ofDate.YearStation.CityStation.CodeStation.LocationStation.StateData.Temperature.Avg TempData.Temperature.Max TempData.Temperature.Min TempData.Wind.DirectionData.Wind.Speed
00.002016-01-03132016BirminghamBHMBirmingham, ALAlabama394632334.33
10.002016-01-03132016HuntsvilleHSVHuntsville, ALAlabama394731323.86
20.162016-01-03132016MobileMOBMobile, ALAlabama465141359.73
30.002016-01-03132016MontgomeryMGMMontgomery, ALAlabama455238326.86
40.012016-01-03132016AnchorageANCAnchorage, AKAlaska343829197.80
50.092016-01-03132016AnnetteANNAnnette, AKAlaska38443198.70
60.052016-01-03132016BethelBETBethel, AKAlaska303624916.46
70.152016-01-03132016BettlesBTTBettles, AKAlaska2232923.10
80.602016-01-03132016Cold BayCDBCold Bay, AKAlaska343631209.10
92.152016-01-03132016CordovaCDVCordova, AKAlaska38433399.76
Data.PrecipitationDate.FullDate.MonthDate.Week ofDate.YearStation.CityStation.CodeStation.LocationStation.StateData.Temperature.Avg TempData.Temperature.Max TempData.Temperature.Min TempData.Wind.DirectionData.Wind.Speed
167330.612017-01-01112017HuntingtonHTSHuntington, WVWest Virginia445335227.68
167340.352017-01-01112017Green BayGRBGreen Bay, WIWisconsin2834212410.53
167350.042017-01-01112017La CrosseLSELa Crosse, WIWisconsin283520259.51
167360.112017-01-01112017MadisonMSNMadison, WIWisconsin273419256.33
167370.152017-01-01112017MilwaukeeMKEMilwaukee, WIWisconsin3138232510.98
167380.082017-01-01112017CasperCPRCasper, WYWyoming2332152319.98
167390.002017-01-01112017CheyenneCYSCheyenne, WYWyoming3242212615.16
167400.002017-01-01112017LanderLNDLander, WYWyoming17294261.65
167410.062017-01-01112017RawlinsRWLRawlins, WYWyoming2331132418.16
167420.102017-01-01112017SheridanSHRSheridan, WYWyoming21348237.51